Estimation of Surface CO 2 Fluxes with Data Assimilation (plus - PowerPoint PPT Presentation

Estimation of Surface CO 2 Fluxes with Data Assimilation (plus latent and sensible heat fluxes) *Ji-Sun Kang, *Eugenia Kalnay, *Takemasa Miyoshi, + Junjie Liu, and # Inez Fung *University of Maryland, College Park + NASA, JPL, # University of California, Berkeley

Success!  With an OSSE, we show it is possible to estimate surface carbon fluxes using 6 hour data assimilation and a realistic observing system.  We use no a priori information.  We could apply the same advanced methodology to assimilate AIRS retrievals and estimate surface fluxes of heat and moisture.

Surface CO 2 flux estimation: top-down approach  Top-down approaches • Estimate surface fluxes from atmospheric CO 2 obs.  Carbon Tracker (Peters et al., 2007) • One of the most advanced top-down approaches • Uses 5 weeks of CO 2 observations: ill-posed problem • A priori information: CASA, eco-regions, etc. • No explicit treatment of transport errors  Alternative approach: Simultaneous analysis of meteorology and CO 2 using LETKF • 6 hours of meteorological and CO 2 observations • No a priori information needed for fluxes • Transport errors are reflected in CO 2 analysis • Observing System Simulation Experiments (OSSEs)

UMD-Berkeley LETKF-C System CF C U V T q Ps CF yes no Forecast Observations C U, V, T, q, Ps, C U, V, T, q, Ps, C U V T LETKF (analysis) U, V, T, q, Ps, C, CF q yes no Ps  Simultaneous analysis of carbon and meteorological variables • Update all variables at every six hours (avoids ill-posedness) • Multivariate analysis with localization of variables (Kang et al., 2011) • Advanced LETKF methodologies • Adaptive inflation (Miyoshi, 2011) and a vertical localization of column CO 2 satellite data

Observing System Simulation Experiments  Nature run (assumed true state in the experiments) • SPEEDY-C: the modified version of SPEEDY (Molteni, 2003) • AGCM with a trace gas of atmospheric CO 2 (C) • Prognostic variables: U, V, T, q, Ps, C • No chemical process of carbon and no diurnal cycle • “True” CO 2 fluxes (CF) • A constant fossil fuel emission (Andres et al., 1996) • CASA terrestrial CO 2 fluxes (Randerson et al., 1997) • Oceanic CO 2 fluxes (Takahashi et al., 2002)  Forecast model • SPEEDY-C with persistence forecast of surface CO 2 fluxes (CF) • CF is updated only by the data assimilation

Simulated Observations  Meteorological variables • Conventional data • U,V,T,q: black dots (every 12 hours) • Ps: gray squares (every 6 hours)  Atmospheric CO 2 concentrations • In-situ & flask observations • Weekly records: black dots (107) • Hourly records: gray dots (18) • Satellite data: column mixing ratios • GOSAT (gray squares) • AIRS (covers half of globe with ascending & descending modes)  No direct measurement of surface CO 2 fluxes

Initial conditions for carbon variables True atmospheric CO 2 near surface True CO 2 fluxes @ initial time @ initial time Initial condition Initial condition of surface CO 2 fluxes of atmospheric CO 2 near surface No initial conditions information! No a-priori information! No surface flux model!

Surface CO 2 flux estimation from LETKF-C  Impact of CO 2 observations on surface CO 2 flux estimation • SFC : in-situ flask data • SFC+AIRS • SFC+GOSAT • SFC+GOSAT+AIRS RMSE of surface RMSE of surface atmospheric CO 2 CO 2 fluxes (ppmv) (gC/m 2 /yr)

Observation impact  Global maps of surface CO 2 fluxes in different seasons C: SFC+GOSAT D: SFC A: True fluxes B: SFC+GOSAT+AIRS

Application to heat/moisture flux estimation  Can we estimate surface heat/moisture fluxes by assimilating atmospheric temperature/moisture observations? We can use the same methodology!  OSSEs • Nature run: SPEEDY • Forecast model: SPEEDY with persistence forecast of sensible/latent heat fluxes (SHF/LHF) • Observations: conventional observations of (U, V, T, q, Ps) and AIRS retrievals of (T, q) • Initial conditions: random (no a-priori information) • Fully multivariate data assimilation • Analysis: U, V, T, q, Ps + SHF & LHF every six hours

Result: Analysis of Sensible Heat Flux

Result: Analysis of Latent Heat Flux

Time series of LHF/SHF 2 1  Black: nature  Color: analysis of LHF(blue)/SHF(red) Recall that LHF & SHF are updated only by the data assimilation here! Promising results from the estimation of “evolving parameters” with data assimilation

Summary  We succeeded in estimating surface CO 2 fluxes with an advanced LETKF-C system, even without any a-priori information (OSSEs)  Dedicated CO 2 monitoring satellite (GOSAT/OCO-2) contribute to the surface CO 2 flux estimation significantly  AIRS CO 2 retrievals help CO 2 flux estimation due to better analysis of atmospheric CO 2 circulation  The same methodology used for carbon cycle data assimilation could be applied to surface heat/moisture flux estimation  AIRS temperature/moisture profile data make possible to estimate sensible/latent heat fluxes over the globe

Briefly: AIRS forecast impacts  Li et al (2010): Impact of assimilating AIRS temperature retrievals (positive).  Liu et al. (2009): Impact of assimilating AIRS moisture retrievals (positive).  Miyoshi and Kunii (2011): Impact of assimilating AIRS T and q retrievals in forecasting typhoon Sinlaku (positive).

Assimilation of AIRS temperature retrievals  System : NCEP GFS (T62L28) and 4D-LETKF  Control Run: All operational observations except for radiances (Non-radiance data, Szunyogh et al. 2007, Whitaker et al. 2007 )  AIRS Run: Non-radiance plus AIRS temperature retrievals [Chris Barnet (NOAA)] v5 emulation with 3 deg * 3 deg resolution & # 2 2 0 0 EXP1 : Ignored retrieval error correlations, $ ! = 2 R 0 2 0 $ ! but increase the error standard deviation to be 2K $ ! 2 0 0 2 % "  Verification: Operational NCEP analysis at T254L64, assimilating all operational observations. (Not “truth”!).

500 hPa Temperature analysis error averaged over Globe Consistent reduction of No AIRS retrievals errors with AIRS retrievals! Non-radiances Non-radiances + AIRS temperature retrieval Result are similar to non-radiance when there are no available retrievals

500 hPa Temperature analysis error SH NH Consistent positive impacts even in the NH! Non-radiances Non-radiances + AIRS temperature retrieval

Impact of AIRS Temperature retrievals on zonal wind 500 hPa Temperature 500 hPa zonal wind AIRS Temperature retrievals also have positive impact on other variables

48 Hour Forecast RMSE SH NH Temperature Geopotential Height Non-radiances Non-radiances + AIRS temperature retrievals

Liu et al: zonal average zonal wind 48-hour forecast RMS error difference between humidity run and the control run Passive q - control Uni-variate q - control multivariate q - control • Uni-variate q has much larger positive impact on 48-hour zonal wind forecast accuracy than passive q (no assimilation) • Multivariate q has largest positive impact on 48-hour zonal wind forecast accuracy

Mi Miyoshi oshi and nd Kuni unii: Impa pact ct of of AIRS RS ret etriev evals s on on for oreca ecast st of of typhoon phoon Si Sinlaku AIRS RS i impro rove ved s significantly t y tra rack f k fore recast and t to s some e ext xtent, i , intensity f y fore recast